PortGPT: Towards Automated Backporting Using Large Language Models

Patch backporting, the process of migrating mainline security patches to older branches, is an essential task in maintaining popular open-source projects (e.g., Linux kernel). However, manual backporting can be labor-intensive, while existing automated methods, which heavily rely on predefined syntax or semantic rules, often lack agility for complex patches. In this paper, we introduce PortGPT, an LLM-agent for end-to-end automation of patch backporting in real-world scenarios. PortGPT enhances an LLM with tools to access code on-demand, summarize Git history, and revise patches autonomously based on feedback (e.g., from compilers), hence, simulating human-like reasoning and verification. PortGPT achieved an 89.15 % success rate on existing datasets (1815 cases), and 62.33 % on our own dataset of 146 complex cases, both outperforms state-of-the-art of backporting tools. We contributed 9 backported patches from PortGPT to the Linux kernel community and all patches are now merged.